{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-05-05T03:34:08.183135Z",
     "start_time": "2024-05-05T03:34:07.841464Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "df = pd.DataFrame(\n",
    "    {\n",
    "        \"Price 1\": [7, 1, 5, 6, 3, 10, 5, 8],\n",
    "        \"Price 2\": [1, 2, 8, 4, 3, 9, 5, 2],\n",
    "        \"Day\": [1, 2, 3, 4, 5, 6, 7, 8],\n",
    "    }\n",
    ")\n",
    "\n",
    "s1 = sns.barplot(x=\"Day\", y=\"Price 1\", data=df, color=\"red\")\n",
    "\n",
    "s2 = sns.barplot(x=\"Day\", y=\"Price 2\", data=df, color=\"blue\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
